Earmark CPE

Earn CPE Anytime, Anywhere

AI

IPA Survey Data Reveals What Best of the Best Firms Actually Do Differently Than the Rest

· ·

“I can’t wrap my brain around how we’re going to utilize technology and make our work more efficient. How do we bill that if we’re billing by the hour? Are we going to start having reduced fees on their invoices? No. So what does that look like?”

That question from Chelsea Summers, Executive Director of Inside Public Accounting, captures the paradox facing the profession right now. Two-thirds of accounting firm revenue still comes from hourly billing, even as AI promises to slash the time it takes to complete work. Something has to give.

On a recent episode of the Earmark Podcast, host Blake Oliver sat down with Chelsea to dig into firm performance data heading into 2026. Inside Public Accounting has been benchmarking accounting firms since 1987. Its latest survey includes over 600 firms, from Deloitte all the way down to firms around $6.5 million in revenue. The numbers tell a story that’s both reassuring and challenging for firm leaders.

The reassuring part is the playbook for outperformance isn’t complicated. Top firms charge what they’re worth, leverage their staff better, and embrace offshore teams. The challenging part is the profession’s attachment to hourly billing might be the single biggest barrier to capturing value from technology investments.

The Best Firms Don’t Work Harder; They Work Smarter

Every year, IPA identifies its “Best of the Best” firms based on 30 different operational metrics. These firms are profitable, but they also have low turnover, succession plans, marketing strategies, and overall organizational health. “Operationally, you’re a high performing firm that’s going to succeed,” Chelsea explained.

The performance gaps between these top firms and everyone else are striking:

  • Revenue per employee: The best firms generate $272,000 per full-time equivalent versus $220,000 for all firms
  • Leverage ratios: Top performers maintain 17.7 professionals per partner compared to 11.8 for average firms
  • Partner billing rates: Best firms charge $588 per hour while others charge $448

That last number deserves emphasis. Top firms are charging $140 more per partner hour, a 30% premium.

But these high-performers don’t necessarily burn out their people to get these results. “The big myth is that high performing firms push people harder, and that’s why they’re making more money,” Chelsea said. “But in reality, those high performing firms often have healthier capacities because they’re using that leverage and they’re using more specialized roles.”

When IPA compared utilization rates and chargeable hours between Best of the Best firms and everyone else, the numbers were nearly identical. Same hours worked, dramatically different outcomes.

The secret is putting the right people in the right roles. Top firms use more client service staff for production work and keep partners focused on partner-level activities like training, business development, and client relationships. When partners step back into production work and start micromanaging, it hurts morale and growth.

Offshoring Has Reached a Tipping Point

Over half of IPA’s survey participants now use some form of offshoring or outsourcing, and less than 5% plan to decrease it. Nearly everyone else plans to grow or maintain their offshore headcount. This is the new normal.

The performance data backs up the strategy. Firms with offshore teams reported 8.1% organic growth versus 7.5% for firms without them. They also saw a 9% improvement in margins.

“Nine percent is a lot,” Blake noted during the conversation. And he’s right. That kind of margin improvement can transform a firm’s economics.

What’s changed is how firms use these teams. The old model treated offshore staff like a processing center for data entry. Today’s successful firms fully integrate offshore team members. They have branded offices, firm email addresses, training opportunities, and direct client communication.

“Really making that individual feel a part of the team is very helpful in correctly utilizing them and making sure they feel the value of working at the firm,” Chelsea explained.

As technology automates the basic data entry tasks that initially justified offshoring, these team members are moving up to manager-level work, supporting advisory services, and contributing to internal operations. The offshore strategy and the technology strategy work together.

Firms Have More Pricing Power Than They Think

One pattern emerged repeatedly in Chelsea’s conversations with firm leaders: they consistently underestimate what clients will pay. “We have all these D and F clients, we want to cull them so we raise their prices 40%. But they all stay,” she shared.

A 40% price increase, and the clients don’t leave. That should make every managing partner pause and reconsider their pricing strategy.

In today’s inflationary environment, not raising prices can actually send the wrong signal. “When your CPA firm doesn’t increase their prices, then you almost say, are they not very good? Do they not believe in their work?” Chelsea observed.

Blake connected this to a broader pattern he’s seen across firms of all sizes. “We talk a lot when we talk about small firms about how they’re underpricing. It’s the same tendency in the midsize and the larger firms where some firms just don’t charge enough. They have pricing power and they’re not using it.”

The Advisory Pivot Is Slower Than Expected

Despite years of conference presentations about the shift to advisory, most firms still generate less than one-third of their revenue from advisory services. Tax and assurance continue to dominate, accounting for about two-thirds of revenue at the average firm.

“That’s really contrary to all the talk that we’re hearing on advisory,” Chelsea said. “I think it is [the future], but the data just isn’t showing that that is yet the predominant model inside most firms.”

Client accounting services, once positioned as the gateway to advisory, are growing but not explosively. Larger firms have shifted their thinking about CAS. “It seems like a lot of firms, especially the larger firms, have shifted away from feeling like that’s a foot in the door to like, that might be a strategy, but that’s not our only strategy going forward,” Chelsea explained.

For firms succeeding with advisory, a few patterns stand out. They have a dedicated internal champion who isn’t juggling 15 other responsibilities. They invest upfront and accept that returns take time. And they recognize that advisory service lines need different processes than tax and assurance work.

AI Faces Cultural Barriers More Than Technical Ones

When Chelsea asks firms about their return on technology investments, the responses are telling. “They’re like, how do we even do that? What does that look like? What does an ROI even mean?”

That said, firms are finding value in specific areas. Tax research stands out as a clear win. Being able to have AI synthesize complex tax code information saves significant time. Workflow automation, document processing, data extraction, and AI-assisted drafting also deliver results.

But adoption is slower than expected, and the blockers are mostly cultural. Partner skepticism leads the list, followed by change management resistance. “The accounting profession is certainly not known for being early adopters,” Chelsea noted.

There’s also a timing problem. Many firms shelved their AI discussions in December for tax season. When they picked them back up in May, there was different software, different models, different capabilities. “You’ve missed all of that research time and possible adoption time just because you’re too busy doing tax season,” Chelsea explained.

We Can’t Ignore the Billing Model Problem Much Longer

Throughout the conversation, Chelsea kept returning to the incompatibility between hourly billing and efficiency gains from technology.

She actually expected the 2025 data to show movement away from hourly billing. Instead, it went slightly in the other direction. Two-thirds of revenue still comes from billable-hour models, and much of what firms call “fixed fee” pricing is just hourly billing in disguise: time estimates multiplied by rates, presented as a flat fee.

Blake shared his own experience to illustrate the problem. When his CAS firm adopted cloud technology early, efficiency gains were 80%. “We couldn’t bill hourly or we’d lose all our revenue,” he said. “We were forced to switch to fixed fees.”

If AI delivers even half those efficiency gains for tax and audit work, firms clinging to hourly billing will face the same reckoning. Except unlike CAS, which was easier to start fresh with new pricing models, tax and audit are where hourly billing is most entrenched.

For firms evaluating technology investments, Chelsea recommends asking three questions:

  1. Does this reduce manual work in a measurable way?
  2. Does it integrate with existing workflows?
  3. Will it free staff to do higher-value work?

If the answers are yes, the investment probably makes sense, even if you can’t calculate a ROI yet.

The Clock Is Ticking

The IPA data paints a clear picture of where the profession stands today. Top performers are executing on fundamentals. They charge appropriately, leverage staff effectively, and embrace offshore teams. Meanwhile, the broader profession remains tied to hourly billing, is moving slowly toward advisory services, and is largely waiting for clearer signals on AI.

For firm leaders, this creates opportunity and urgency. The playbook for better performance isn’t complicated, but the window to adapt might be narrowing. Firms that figure out how to decouple revenue from hours worked will be positioned to benefit from technology investments. Those that don’t may watch their revenue shrink as efficiency gains eat into billable hours.

“I’m crossing my fingers that 2026 we’re going to see some change,” Chelsea said about the billing model evolution. Given what’s at stake, the entire profession should be crossing their fingers with her.

For a deeper dive into these insights, including specific benchmarks on compensation trends, capacity planning, and technology adoption, listen to the full conversation between Blake and Chelsea on the Earmark Podcast. You can earn free NASBA-approved CPE credit for listening.

The Month-End Close Is Accounting’s Biggest Bottleneck. Here’s How AI Is Dismantling It

· ·

The day before a tax deadline, and accountants from Miami to Vancouver, Portland to New York, logged into a CPE-eligible webinar to learn something that could fundamentally change how they work. The webinar showed how these professionals can shrink the most time-consuming part of their month-end close, like reconciliations, transaction coding, and bank statement chasing, from days to minutes.

Megan Reid, product specialist at Digits, led the session, and she brings a unique perspective. She’s an accountant with 15 years in the trenches, starting at a Big Four firm, moving through banking and construction, and now helping firms build what she calls an “AI-native” practice. As she put it to the audience, “As accountants, we want to be able to serve more clients, provide better service, and do so quickly and efficiently.”

The traditional month-end close is accounting’s biggest bottleneck. It’s that manual grind through booking transactions, reconciling statements, updating schedules, reviewing anomalies, and (if there’s time left) analyzing the numbers and creating the reports clients care about. “It’s a manual, tedious, time-consuming process that honestly leaves a lot to be desired for both the business owners and the accountants,” Megan said bluntly. 

But what if you could flip that entire workflow? What if instead of reviewing every transaction, you only touched the ones AI couldn’t confidently handle? That’s exactly what Megan demonstrated live, showing how AI-native platforms transform the close from a compliance chore into an opportunity for real advisory work.

The bottlenecks killing your efficiency

Before diving into solutions, Megan mapped out where the traditional close breaks down. You start in QuickBooks or your ledger of choice, but quickly find yourself bouncing between Excel, browser tabs for vendor research, your close management tool, and who knows what else. “Not only are you managing the work across all these multiple platforms,” she explained, “you’re also spending time validating sync accuracy, troubleshooting issues, and making sure the data moves seamlessly throughout the various systems.”

Each phase has its own special frustrations:

  • Manual data entry and rule management introduce human error
  • Fighting with bank access and chasing clients for statements
  • Disconnected tools for AP, credit cards, and close management
  • Team members use different processes, causing rework and confusion
  • Manual journal entries pile up at period-end

As a result, most of your time goes to necessary but low-value tasks, leaving little room for the analysis and insights your clients actually hired you to provide.

How AI learns your way of doing things

The shift to AI-native platforms involves intelligence that learns and adapts. When Megan pulled up the demo client in Digits, she showed hundreds of transactions the AI automatically categorized. Only eight were flagged for review.

“How does it know how to categorize transactions?” she asked, anticipating the obvious question. The answer lies in three layers of learning.

First, there’s client-level learning. When you correct a categorization for a specific client, the system learns instantly. “If you review something for a brand new client and you say, ‘nope, you categorized this to software, but I actually want it to be cost of revenue,’ Digits learns from that instantly,” Megan explained.

Second, there’s firm-level learning. The system recognizes patterns across your entire client base. If the system does not have the client-level layer of knowledge, it falls to the firm-level. “How has my firm done this across all of my clients? It automatically applies your firm’s unique value to your client base.”

Third, when a transaction is entirely new, proprietary models trained on billions of dollars’ worth of transactions make the call.

During the live demo, Megan reviewed a U.S. Patent and Trademark Office transaction the AI thought might be taxes. She looked at the suggestions (taxes, legal, or a new intangibles account), selected “Legal,” and clicked save. The system immediately found two similar transactions and updated them automatically. The review queue dropped from eight to five in seconds.

But what really eliminates busywork is the AI agents run 24/7 in the background, researching vendors and populating details. “None of this has been populated manually,” Megan showed, clicking through a vendor profile complete with name, logo, description, and related websites. “We’re essentially researching them and populating all of the data for you.”

Bank reconciliation without the chase

If transaction categorization is tedious, reconciliation might be even worse. You know the drill: fighting for bank access, emailing clients for statements, then manually comparing the ledger to the statement line by line.

Megan demonstrated the “happy path” first. Digits pulled a Mercury bank statement via an API, automatically kicked off reconciliation, matched every transaction with pixel-level precision on the PDF, confirmed the ending balance, and finalized everything. Zero human touches required.

“Some firms we work with actually say, ‘I uploaded six months of bank statements and just watched them finalize one by one. And I didn’t do anything,'” Megan shared.

When auto-reconciliation can’t finalize completely, it doesn’t leave you guessing. The system flags specific issues, such as:

  • Missing transactions that exist on the statement but not in the ledger (one click to create)
  • Date mismatches where something cleared May 31 but hit the ledger June 1 (one click to adjust)
  • Unsettled items like checks that haven’t cleared yet

For banks without API access, such as small credit unions, you simply drag and drop a PDF statement. During the demo, Megan dragged a statement into the system and watched it extract data and start reconciling in seconds.

She took it further with a cleanup scenario. Starting with a brand-new bank account, she imported a PDF statement. Within moments, 14 transactions appeared as uncategorized. Seconds later, the AI had populated every vendor name and category without a single manual input.

Turning saved time into client value

Speed alone isn’t the point. As Megan emphasized, “the compliance and the month-end close is really just a means to an end,” the end being insights and value for clients.

The dashboards in Digits default to the current month because, as Megan noted, “knowing something two months late doesn’t usually help.” Every metric is live and drillable. Click into gross income, and you see the definition, calculation, and every underlying transaction. Your clients finally understand how you arrived at the numbers.

Each client gets customized dashboards. “Maybe you have a client that’s like, ‘we’re spending so much money on travel,'” Megan explained, showing how to add customized metrics that are specific to each client. A profitable client with ten years of runway might swap that widget for gross profit or vendor analysis.

Collaboration happens right on the platform. On any transaction, category, or report, you can leave a question. The client receives a notification and can respond directly from email without logging in. “One of the biggest pain points is transfer of knowledge,” Megan said, “making sure that you have everything that you need from your clients and vice versa.”

Custom reports become interactive stories rather than black-and-white PDFs. The AI generates insights like “You earned 33% more in March compared to the prior month” with drill-down capability to see exactly why. Important insights can be pinned to the executive summary so they’re the first thing clients see.

What this means for your firm

During the Q&A, attendees asked practical questions. One wondered if this integrates with QuickBooks or replaces it entirely. “Digits is a complete ledger system. So it’s a complete replacement,” Megan answered. They can migrate QuickBooks data in about two minutes, but this is a ground-up rebuild, not a bolt-on tool.

Another attendee asked about company scale. The focus is on small and medium-sized businesses, which is the client base most firms serve.

The shift from reviewing everything to reviewing only exceptions makes the close faster and more consistent across your team, less error-prone, and it frees up capacity to serve more clients without hiring proportionally.

“It’s a very exciting time to be an accountant while also a little bit scary,” Megan acknowledged near the session’s end. “I think it’s a time to really lean in and be excited.”

She’s right. The firms embracing AI-native tools now will deliver premium advisory services while their competitors are reconciling bank statements at midnight.

To see these workflows in action, watch the full webinar. Every accountant who signs up gets access to a sandbox demo environment where you can test these workflows with real data. And if you attended live or watch the recording, you can earn CPE credit through the Earmark app. Just search for the course and complete the quiz.

The close is changing. Will you lead that change or follow it?

The Billable Hour Is Broken and Every Firm Leader Knows It. So Why Won’t Anyone Kill It?

· ·

Richard Lynch can list every reason the billable hour is broken. It undervalues experienced professionals, creates perverse incentives, burns people out, and reduces human beings to time-tracked units. But without a hint of irony, he admits that Sikich, one of the largest CPA firms in the country, still tracks hours “on a religious basis.”

That contradiction tells you everything about where the accounting profession stands right now.

On a recent episode of the Earmark Podcast, host Blake Oliver sat down with Richard, a managing principal at Sikich with over 25 years in public accounting. They had an honest conversation about where things actually stand. Not where the conference keynotes say they stand or where vendor demos suggest they stand, but where they actually stand inside accounting and advisory firms, at the level where someone still has to fill out a timesheet at 9 p.m. on a Tuesday.

The takeaway is that the profession’s transformation is stalling because firms can’t let go of the operational scaffolding they’ve built around the billable hour.

The Super Accountant Vision

Richard has a term for what’s coming: the “super accountant.” It sounds like marketing language, but his definition is specific. A super accountant has AI fluency, strong judgment, and understands compliance without needing to physically perform it. “They’re not a tech person doing accounting work,” Richard explains. “They are a technical person, maybe a CPA, that specifically knows how to leverage technology.”

The structural change everyone talks about is the pyramid becoming a diamond with fewer people at the entry level and more in the middle. But Richard makes an important distinction. The bottom rung is moving up in capability, not disappearing. Future CPAs will “reach a higher level of intellect, capability, and advisory skills at a much earlier age without decreasing the standards.”

Richard points to how this has happened before. Thirty or forty years ago, interns got coffee and made copies. Today, interns do actual client work. “The capability of interns moved up,” he says. The same shift is about to happen again, just bigger.

But the education system isn’t ready. Accounting programs are mostly theoretical. They “lay a foundation,” Richard says, but “certainly don’t give you anything that is pretty or accessible to a client.” Firms will have to bridge the gap with intensive training, it may look like six to eight months where new hires don’t touch billable work, just learn the craft.

The Review Problem

Blake raises the concern many accountants have voiced. If AI does all the basic work, how do people learn to review? The whole system depends on doing the work first, making mistakes, getting feedback, and building judgment.

Richard doesn’t dismiss this. He calls it “a real concern” and says you “can’t underestimate or understate the value of experience.” But then he reframes it with an analogy.

Try explaining to kids today why they need to know how to use an encyclopedia. It seems absurd. The skill became irrelevant because the tool changed. What replaced it was arguably harder: filtering reliable information from unreliable information online.

The same thing is happening with review. “Technology will take care of putting it in the proper box,” Richard says. “Your objective is to have the filter of understanding how to interpret the outcome.” And he goes further: “There may be a benefit to actually not having that anchor of how we used to do business.”

This isn’t theoretical. Tax GPT claims it fully automated 1040 preparation. Basis says it’s done the same for partnership returns. Richard has talked to both vendors. The pace of accuracy improvement is “impressive.” AI is rapidly getting to where it’s “right more than it’s wrong.”

But Richard draws an important distinction. Completing a tax return is just compliance. The real product is what happens after: the advice on paying less tax, structuring a business sale, or planning succession. “When you engage with your clients beyond delivering compliance services,” Richard notes, “fees don’t really come up.”

Why the Billable Hour Won’t Die

“Our people hate entering their time,” Richard says plainly. “There’s no value to the time they spend entering their time and it undervalues us.” Experienced professionals solve complex problems in an hour because they have 30 years of experience. Bill that as one hour, and you’re “undervaluing the 30 years of experience that allowed you to answer that question.”

Richard calls abandoning timesheets “the Mount Everest” of firm transformation. The billable hour is the operating system. Everything runs on it, including utilization, productivity, margin, capacity planning, performance evaluation, even work-life balance monitoring. “You can’t really erase billable hours without erasing all of it,” he says.

Then Richard makes an argument Blake hadn’t considered before. Timesheets might actually help prevent burnout. Sikich monitors employees running over their expected hours and treats it as a capacity problem. Without those guardrails, Richard argues, ambitious people “will work so hard, they’ll burn themselves out really quickly.”

But Blake zeros in on the real issue. AI has destroyed the link between time and value. If AI makes your team twice as fast, the client pays half as much under hourly billing. That math doesn’t work anymore.

So what replaces hours? “We haven’t necessarily identified a better alternative,” Richard admits. Accountants like data and hours provide lots of data. Any replacement becomes more subjective. Client satisfaction? Value delivered? Team engagement? These are harder to measure, and for a profession built on measurement, that’s a problem.

The Basketball Team Problem

Richard draws on his sports background to explain what might work better. Think about the 1990s Chicago Bulls. Michael Jordan and Scottie Pippen scored the points. But Dennis Rodman, the defensive specialist who didn’t score much, was essential. His contribution didn’t show up in the headline stats, but the team needed him.

“We’re not even looking at points. We are looking at time on the court.” Blake points out. The profession measures the wrong thing entirely.

But Richard warns that team models only work if everyone performs. If Rodman doesn’t hustle for rebounds while Pippen is scoring, or if Pippen takes a game off while Rodman is sacrificing his body, the whole thing falls apart. “You have to have a culture where the team performs within kind of a standard deviation of each other.”

The deeper problem is cultural. “The connotation of the employee becomes, I am an hours-based person. All I am is hours,” Richard says. When every review, promotion, or conversation starts with “how many hours did you work,” people internalize that their value is their time. Not their judgment or ideas.

And the system treats every hour as equal, which Richard calls “baseline, categorically false.” Some people think faster. That doesn’t make them more valuable, but under an hours system, it makes them look more productive.

The Implementation Gap

Richard says people actually don’t burn out from long hours. “I don’t hear complaints about the hours when it’s engaging work,” he says. He says his team gets excited working a long weekend for a complex client issue. The burnout comes from being stuck at 9 p.m. “dealing with software issues and plugging numbers into spreadsheets.”

AI can eliminate that burnout-causing work. But only if firms actually let it.

“We’re playing with it, but we’re not really implementing it,” Richard says. “We’re purchasing it, but we’re not really relying on it.” Firms pour billions into AI tools, but their training, career paths, and daily operations haven’t changed. The technology is there but the willingness to break old processes isn’t.

“There will be progression and there will be extinction. The question is at what pace,” Richard says, framing the stakes clearly.

Working harder won’t compensate for failure to adopt anymore. Buying AI products doesn’t mean you’re adopting AI. And trying to fit AI into existing processes instead of letting it break them is a choice with consequences.

“If you consistently try to find a place of complacency and comfort, you will not adopt at the pace necessary,” Richard warns.

The Choice Firms Are Making Right Now

What makes this conversation valuable is Richard’s willingness to acknowledge he doesn’t have all the answers. “I still have a lot to learn,” he says.

He can see the billable hour is broken and the pyramid is unsustainable. He can see buying AI tools without changing operations is theater. And Sikich is still tracking hours religiously.

That honesty tells you where the real work is. The super accountant future requires dismantling training models, educational assumptions, and measurement systems that have existed for decades. Not just purchasing new software.

For accounting professionals at every level, including partners making decisions, managers caught between old metrics and new realities, or someone early in their career wondering what’s ahead, the question is whether the firm will let AI change your work.

Richard has a message for other firm leaders: “Don’t let fear rule the day.” The firms that use AI as permission to break outdated processes will thrive. The firms that bolt AI onto unchanged operations will struggle. And that divergence is accelerating.

“I have every desire to be on the side of progression,” Richard says. Which side is your firm choosing?

Listen to the full conversation between Blake and Richard on the Earmark Podcast for deeper discussion on replacement metrics for the billable hour, building the super accountant pipeline, and why letting go of the past might be the profession’s biggest challenge. Then visit earmark.app to earn free NASBA-approved CPE credit.

When Tax Day Was Party Night at the Post Office — And Why AI Is About to Upend Everything Else About Accounting

· ·

Before tax e-filing took over, April 15th was a public spectacle at American post offices. As Blake Oliver and David Leary discussed on their Tax Day episode of The Accounting Podcast, crowds would gather until midnight, with live entertainment, giveaways, and even Playboy offering “stress relief massages” in pink booths. In Philadelphia, there was a “dunk the IRS agent” booth for charity. Radio stations broadcast live. Fast food chains handed out samples. It was America’s weirdest annual party.

Those days are gone — 94% of returns are now filed electronically. But as the hosts explored in this wide-ranging episode, the accounting profession faces disruptions far more profound than the shift from paper to pixels. Within three years, KPMG expects routine audit testing to have “next to no human beings” doing the work. Hobbyist developers are cloning QuickBooks with AI over a weekend. And a third of workers aren’t even checking AI outputs before they submit them.

The IRS Can’t Keep Up — With Rules or Technology

The profession’s struggles with rapid change start at the top. Just five days before the filing deadline, the IRS finalized which jobs qualify for the new no-tax-on-tips deduction. Podcasters made the cut (Oliver and Leary were pleased), along with tattoo artists, ice sculptors, and golf caddies. Accountants didn’t.

“Five days after they finalized these rules to implement them for our clients,” Oliver noted with frustration. The deduction allows eligible workers to exclude up to $25,000 in tips from taxable income, but mandatory service charges don’t count. “This could be the death of the automatic gratuity,” Leary speculated, since those forced tips won’t qualify.

Meanwhile, Americans are spending 11.6 billion hours completing federal compliance forms — mostly tax returns. The value of that labor? Over half a trillion dollars. “That’s material,” Oliver said, noting it represents a significant chunk of the economy devoted to paperwork.

The IRS’s own modernization efforts tell a cautionary tale. The agency had 126 AI projects running as of last summer, up from just 10 in 2022. But after losing 25% of its workforce, 61% of those projects remain unfinished with no plan to close the skills gap. Even more puzzling: the IRS killed its Direct File program despite it costing only $16 million instead of the estimated $61 million and growing 78% year-over-year. “The program was gaining traction and was less expensive than they thought it was going to be, and yet it got canceled anyway,” Oliver observed.

The Big Four’s Radical Restructuring

While the IRS struggles with basic modernization, the Big Four are racing ahead with AI automation that could eliminate thousands of jobs and upend the billable hour model that has defined the profession for decades.

KPMG is moving fastest. They’re piloting AI systems this summer and deploying them next year for routine testing of transactions like payroll, receivables, and cost of goods sold. “Within 2 or 3 years, routine testing could become the first major audit area with effectively no human audit team directly doing the work,” Oliver quoted from KPMG’s audit chief digital officer. “Next to no human beings.”

The other firms aren’t far behind. PwC’s evidence-matching tool now processes 30 client document types, up from six months ago. EY is testing something even more futuristic: AI audit agents that talk directly to client AI agents to gather documents and prepare workpapers. Only Deloitte is publicly pumping the brakes, emphasizing AI should “augment not replace” human auditors.

The numbers are stark: Big Four leaders expect 20-30% of a typical audit to be fully automated by 2029. KPMG UK is already cutting 440 audit jobs. “I don’t see any other outcome than the Big Four just cutting massive numbers of staff jobs,” Oliver said. “If they do this right… that’s 20 to 30% of their billable hours. What are they going to do? Just raise their rates 20 to 30% to compensate?”

Leary had the line of the episode: “Agents are the perfect accounting firm employees. The partners are going to love them.”

The traditional career path is crumbling too. EY’s talent chief told Business Insider that linear career models are becoming “less relevant” as AI values skills over tenure. Oliver speculated firms might shift from hiring masses of new graduates to recruiting experienced professionals from industry, or moving to an apprenticeship model with smaller, more intensively trained classes.

Everyone’s Building Their Own QuickBooks Now

The disruption isn’t just coming from the top. A Reddit user built a full accounting system that runs inside Claude Desktop — no interface, just chat. You tell Claude what happened, and it updates your books. Another developer cloned QuickBooks Desktop using AI, creating a free open-source alternative. The motivation? “I didn’t want to pay for QBO.”

“You as an accounting firm had control over your tech stack and your clients’ tech stack,” Leary explained. “We’re a Xero shop or a QuickBooks shop… Now your clients are just building their own stuff. How do you as a firm manage this now?”

Oliver’s prediction, based on every past tech revolution: “We will end up with more work rather than less, because it will enable our clients to do way more accounting stuff that we’ll have to clean up.”

On the funded startup side, Juno raised $12 million to build AI tax prep that automates 90% of data entry while keeping CPAs in the loop. The key: transparency over autonomy, with source-to-return traceability and visual validation tools. Artifact launched Omni, which Leary called “a Zapier for accounting firms” — it trains AI agents to use your existing tech stack rather than replacing it.

Meanwhile, legacy players are scrambling. Xero published a blog post claiming to be an “AI native operating system.” Leary counted over 20 buzzwords and read them aloud in a devastating list: “AI native, intelligent SaaS, autonomous finance, system of action…” His verdict: “I don’t think this is written for customers. I think this article is written for the street in an attempt to move the stock price.”

The Quality Crisis Nobody’s Talking About

Here’s what should terrify every firm leader: 35% of workers rarely or only occasionally review AI output before submitting it, according to a Resume Now survey. Eighteen percent trust it straight out of the box. Only 40% review AI output every single time. And 15% use AI at work secretly without telling their manager.

“That should scare you as an accounting firm owner,” Leary said.

Oliver argued firms need systems with built-in controls: “If an employee is just generating something with AI… and they didn’t change anything or they didn’t spend any time looking at it, then flag that.”

The stakes are real. The episode covered two fraud cases that show what happens with weak oversight. A New Jersey preparer filed over 100 false returns seeking $170 million in pandemic credits, getting $55 million before being caught. A Pennsylvania preparer started a new $5.5 million fraud scheme while still on supervised release from a previous conviction.

What Separates Winners from Losers

A Hinge Marketing study of 133 firms revealed a massive performance gap emerging. High-growth firms are growing at 33% annually versus 9.6% for average firms. The difference? High-growth firms spend 9% of revenue on marketing (versus 5% for others), and over 90% use AI for content creation, automation, and research.

“If you have a firm that’s growing at 10% and you want it to grow at 30%, spend 10% of your revenue on marketing,” Leary summarized, though Oliver questioned whether it’s causation or correlation: “Is it just that the firms that are growing really fast have money to burn on marketing?”

The Reckoning Is Here

The accounting profession has always adapted slowly. As Leary noted, “Just ask Xero how it takes decades for them to barely make a scratch into the QuickBooks world.” But this time feels different. The changes are coming from every direction at once — Big Four automation, bedroom coders, funded startups, and clients building their own systems.

The irony is thick. Even as AI promises to make location irrelevant, EY is requiring US tax staff to work in-office 12 days a month. The IRS has 126 AI projects but can’t finish them. Firms are adopting AI while a third of workers don’t even review its output.

For firms willing to invest, experiment, and build proper controls, the opportunity is massive. For those hoping to wait it out, the message from this episode is clear: the profession that gathered at post offices until midnight to file paper returns is gone. The question isn’t whether AI will transform accounting — it’s whether the profession can maintain its core promise of trustworthiness while everything else changes around it.

To hear the full discussion — including the story of a disgruntled worker who burned down a $500 million Kimberly-Clark warehouse over pay disputes — listen to the complete episode of The Accounting Podcast.

Not All AI Is Created Equal and Your Next Software Decision Depends on Knowing the Difference

· ·

When Jeff Seibert ran consumer product at Twitter, he asked the finance team for his budget to throw a team event. They said they’d get back to him in 45 days. So he just ran the event without them.

That gap between real-time data and 30-to-90-day delayed financial reports was frustrating, and it eventually led Jeff to build Digits, a new general ledger designed from scratch for the machine learning age. After raising $100 million pre-launch, testing 2,000 monthly closes, and getting 80% of clients closed in under an hour, Digits launched in March 2025. Now, just over a year later, hundreds of accounting firms are onboarding thousands of clients onto the platform.

Jeff launched Twitter’s algorithmic timeline in 2016, and it was one of the first global deployments of machine learning. Now, the AI revolution Jeff helped launch is flooding the accounting profession with claims that are hard to verify. Every accounting software company seems to include AI in its marketing copy, promising everything from “fully automated bookkeeping” to capabilities that don’t add up under scrutiny.

In a recent Earmark webinar, host Blake Oliver and Rob Hamilton, Head of GTM at Digits, pulled back the curtain on how AI in accounting actually works. He was joined by Megan Reid, Product Specialist & Firm Enablement at Digits, who fielded questions throughout the session.

Every AI claim in accounting software isn’t real. But accountants who understand the four core model types (plus one common lie) will make smarter investments, automate the right parts of their workflow, and position their firms for a shift Rob sees coming by the end of 2026.

The AI hype problem (and one question to cut through it)

Before making any technology decision, you need a filter for separating real capabilities from marketing fluff. Rob offered a simple one that cuts through the noise.

He showed screenshots from multiple accounting software companies making bold AI claims. One promised “fully automated bookkeeping.” Another asked, “Do you do AI bookkeeping or do you use a dedicated team of experts?” The positioning has gotten so confusing that firms can’t tell what’s real anymore.

The confusion isn’t new. About five years ago, tech investor Naval Ravikant tweeted, “In most pitch decks, AI stands for Anonymous Indians.” For a long time, that was literally true. Services like Botkeeper rose and fell using offshore labor dressed up as automation. Today, “AI actually means we just bolted on and sent all of your data to ChatGPT,” Rob explained.

Here’s your filter: “AI is the same thing as machine learning,” Rob stated. “If someone is talking to you about AI and they’re not referring to machine learning as the underlying premise, it’s just BS.”

But this filter only works if you understand what machine learning actually is.

Traditional software is straightforward. You write code that tells the computer exactly what to do. It’s tedious to build, but rock solid once it works. Machine learning flips this completely. You feed the system thousands or millions of examples, and the model learns the patterns itself. As Jeff explained in a clip Rob played, “You give the computer the goal state—I want this outcome—and then the computer itself is learning how to do it.”

These models are neural networks. Thousands of hidden layers mimic how neurons connect, based on Google’s 2017 “transformer” research paper (the “T” in GPT). It’s a massive matrix multiplication problem where the system figures out how variables relate to each other.

But machine learning isn’t one thing. Different model types have different strengths and uses in accounting. Understanding these distinctions helps you avoid buying the wrong software and shows you exactly where AI can save time and where vendors are overselling.

The model types that matter (and one that doesn’t)

Rob walked through five categories that get lumped under “AI,” but understanding the differences is what separates informed decisions from expensive mistakes.

Generative models

Large language models (LLMs) are the ones you hear about most, ChatGPT being the prime example. GPT stands for “Generative Pre-Trained Transformer,” and these models generate the most likely continuation of whatever prompt you give them. Rob showed a useful application: turning bullet-point close notes into polished client emails. His advice is to write a “job description” for the AI once. Tell it who it is, give context, specify output format, add examples. Then just paste in different client notes as needed.

But generative models have serious limits. They’re “super eager” and always want to complete prompts, making them prone to hallucinations, or making things up that sound real. They’re bad at math because they generate text rather than calculate numbers. And they’re trained on the internet, not your specific clients. “The ways that it is hallucinating is stuff that maybe even is hard for humans to catch sometimes,” Rob warned. Always review the output.

Agents

These are LLMs with help. You give them a job description, a task, and tools, like computer programs they can use to generate reports, list accounts, or run calculations. The agent makes a plan, uses its tools, checks if the task is done, and loops until complete. Rob showed Digits’ agent answering “I want to hire 20 software engineers next year. Can I afford to?” with a data-backed response.

Guardrails are critical. Microsoft’s early agent “started asking people on dates in the chat,” Rob noted. “You don’t want your accounting agent dispensing dating advice.” Agents work well for updating schedules, running quality checks, and answering analytical questions, but they’re slow and need careful boundaries.

Predictive models

These got Rob visibly excited, and for good reason. These models take an input and predict an output from known options. When the model sees a $5 Starbucks charge, it considers the client’s location, history, and chart of accounts. For a local client, it’s meals and entertainment. For a New York client in California, it’s travel. A $157 Starbucks charge is probably an event, regardless.

What makes predictive models perfect for transaction categorization is they can’t hallucinate; they only choose from existing options. They’re deterministic (same input, same output), include confidence scores, and run fast and cheap once trained.

Digits built a “layer cake” of predictive models:

  1. Client-level (learns each business)
  2. Firm-level (encodes your best practices)
  3. Global (trained on 180 million transactions worth nearly $1 trillion)
  4. An LLM fallback for completely new transactions

The result was over 97% accuracy, compared to standalone LLMs that plateau below 80%, which is about the same as outsourced bookkeepers.

Document extraction models

These combine OCR with layout-aware language models that understand document structure. Previous tools used Amazon’s Mechanical Turk, which relied on humans manually extracting data and took hours. Modern extraction models work in seconds. Digits’ bank reconciliation automatically pulls PDF statements, matches transactions to the exact spot in the PDF, and generates audit reports.

Data analysis

This model is where Rob pulled the rug out. Financial reporting and analysis is “actually just math. It’s not ML.” Computers have done statistical analysis for decades. Could you build an agent to do it? Sure, but it would be slow, expensive, and probably wrong. “If anyone says their AI does reporting and statistical analysis, please ask them what they’re talking about.”

Here’s how the right models map to your month-end close:

  • Book transactions: Predictive models (with LLM fallback)
  • Reconcile statements: Extraction models plus matching algorithms
  • Update schedules: Agents
  • Review and correct: Agents with quality checklists
  • Analyze and report: Statistical analysis plus agents for questions

“Shoehorning an LLM in to solve a problem and just sending a bunch of information is fundamentally incorrect,” Rob emphasized. Each step needs the right model. No single AI approach handles everything.

The 2026 prediction

Understanding model types is just the foundation. The urgency comes from how fast everything is converging.

“Across a large client set in different industry types, it’s highly likely that the month-end close process is looking to be completely automated by the end of 2026,” Rob predicts. Even his “95% automated” hedge probably sounds aggressive. But his logic follows directly from the technology.

If predictive models hit 97% accuracy on transactions, extraction models automate reconciliation in seconds, agents handle schedules and quality control, and statistical analysis covers reporting, then manual work drops to a fraction. Rob’s goal is to see accountants doing “1/20th of the work you’re doing today.”

He acknowledged limits. Construction firms with complex job costing might not hit that threshold. But for firms serving professional services, cash-basis businesses, and straightforward accrual clients, the automation curve is steep.

An AI-native firm will focus on value instead of tedium. Deeper industry expertise. Stronger client relationships. Higher margins. You’re not reviewing every transaction, you’re supervising the system and handling exceptions. Those hours saved on reconciliation become advisory time clients actually value.

But this is also a competitive necessity. “AI won’t replace you. Someone who’s good at using AI is going to,” Rob said, quoting a common warning. And he was direct about the stakes. Firms that don’t adapt will face “a cascading effect on business models” as early adopters pull ahead.

For overwhelmed firms, which Rob acknowledged includes most firms, he offered practical starting points:

  • Map your processes first. If you use workflow tools like Karbon or Keeper, you’ve probably documented your steps. If not, start there. You can’t identify where AI fits until you know what you’re actually doing.
  • Start small and low-stakes. Don’t tackle your biggest challenge first. Try drafting emails, testing categorization, or visualizing data. Build your intuition gradually.
  • Get hands-on with new tools. Rob mentioned being impressed by Claude Opus, which could build HTML dashboards from his data (something he couldn’t do as a non-engineer). The specific tool doesn’t matter; hands-on experience builds judgment.
  • Know your business before choosing where to start. As Rob put it, “You need to know the details of your business to know where you can start and where the right places to poke and prod are.”

The “wait and see” window is closing. Firms that develop AI literacy now by asking questions about models, data handling, and use cases, will be ready for the rest of 2026 and beyond.

Your next move: Better questions, smaller steps, faster action

Let’s turn Rob and Megan’s insights into actionable takeaways:

  • Not all AI is equal. Four real model types plus one fake (data analysis) get lumped together. When vendors pitch “AI-powered reporting,” you now know to dig deeper.
  • Each close step needs a different model. Predictive for transactions. Extraction for reconciliation. Agents for schedules. Statistics for reporting. Anyone claiming one solution does everything deserves scrutiny.
  • Predictive models beat LLMs for categorization. Layered architectures that learn your clients and firm patterns dramatically outperform chatbots. Bigger isn’t always better.
  • Ask vendors the hard questions. What model type? Where does data go? Are you training your own models or sending financial data to third parties? This is due diligence.
  • The tipping point is closer than you think. Whether Rob’s 2026 prediction proves exactly right or directionally right, the trajectory is clear. Understanding these distinctions now positions you to take action.

For those interested in going deeper, Rob mentioned resources like the AI Native Accounting Foundation and the AI-Native Accounting podcast hosted by Kacee Johnson, where industry leaders discuss the latest developments.

The accounting profession is at a real inflection point. Smart firm leaders will develop the literacy to ask smart questions, experiment in the right places, and redirect time from tedium to advisory work clients value.

Rob noted this might be one of the few professions with such clear AI use cases, putting accountants at the forefront of innovation. That’s an opportunity to shape how technology serves the profession, not the other way around.

Watch the on-demand webinar for complete details, including live demonstrations, security architecture specifics, and audience Q&A covering nonprofits, inventory clients, and platform migrations. The future of accounting is being written now. Make sure you’re part of the conversation.